{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                               Sentence  SUBJpolit\n",
      "0     and jane confident story its going come back a...          1\n",
      "1     click expand nick esposito yearold skydiving i...          1\n",
      "2     its still early story even remdesivir approved...          1\n",
      "3     this image released apple tv plus show tom han...          1\n",
      "4     kurtulmu also said constantly calling internat...          1\n",
      "...                                                 ...        ...\n",
      "6365  speaking cnn last week herald square midtown m...          2\n",
      "6366  these gigantic structure made countless clump ...          1\n",
      "6367  the solar orbiter spacecraft beamed back close...          2\n",
      "6368  the vote today nothing liz cheneys vote impeac...          2\n",
      "6369  but theres even depth tree empty socket slotte...          1\n",
      "\n",
      "[6370 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sentence</th>\n",
       "      <th>SUBJpolit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>and jane confident story its going come back a...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>click expand nick esposito yearold skydiving i...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>its still early story even remdesivir approved...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>this image released apple tv plus show tom han...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>kurtulmu also said constantly calling internat...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6365</th>\n",
       "      <td>speaking cnn last week herald square midtown m...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6366</th>\n",
       "      <td>these gigantic structure made countless clump ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6367</th>\n",
       "      <td>the solar orbiter spacecraft beamed back close...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6368</th>\n",
       "      <td>the vote today nothing liz cheneys vote impeac...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6369</th>\n",
       "      <td>but theres even depth tree empty socket slotte...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6370 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Sentence  SUBJpolit\n",
       "0     and jane confident story its going come back a...          1\n",
       "1     click expand nick esposito yearold skydiving i...          1\n",
       "2     its still early story even remdesivir approved...          1\n",
       "3     this image released apple tv plus show tom han...          1\n",
       "4     kurtulmu also said constantly calling internat...          1\n",
       "...                                                 ...        ...\n",
       "6365  speaking cnn last week herald square midtown m...          2\n",
       "6366  these gigantic structure made countless clump ...          1\n",
       "6367  the solar orbiter spacecraft beamed back close...          2\n",
       "6368  the vote today nothing liz cheneys vote impeac...          2\n",
       "6369  but theres even depth tree empty socket slotte...          1\n",
       "\n",
       "[6370 rows x 2 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import re\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import SnowballStemmer\n",
    "from sklearn import feature_selection\n",
    "import pymongo\n",
    "import os\n",
    "from sklearn import feature_extraction, model_selection, naive_bayes, pipeline, manifold, preprocessing\n",
    "stop = stopwords.words('english')\n",
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\"\n",
    "from tqdm import tqdm\n",
    "import joblib\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "import string\n",
    "df = pd.read_csv('../data/version2.csv',encoding = 'ISO-8859-1')\n",
    "# cols = [col for col in df if not col.startswith('Unnamed:')]\n",
    "# df = df[cols]\n",
    "print(df)\n",
    "b_set = df\n",
    "b_set['SUBJpolit'].value_counts()\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "b_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## split dataset\n",
    "#dtf_train, dtf_test = train_test_split(b_set, test_size=0.3)\n",
    "## get target\n",
    "#y_train = dtf_train['SUBJpolit'].values\n",
    "#y_test = dtf_test['SUBJpolit'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Count (classic BoW)\n",
    "vectorizer = feature_extraction.text.CountVectorizer(max_features=10000, ngram_range=(1,2))\n",
    "\n",
    "## Tf-Idf (advanced variant of BoW)\n",
    "vectorizer = feature_extraction.text.TfidfVectorizer(max_features=10000, ngram_range=(1,2))\n",
    "XChi_train, XChi_test, YChi_train, YChi_test = train_test_split(b_set['Sentence'],b_set['SUBJpolit'].astype('int'),test_size=0.3, random_state=3)\n",
    "\n",
    "corpus = XChi_train\n",
    "vectorizer.fit(corpus)\n",
    "X_train = vectorizer.transform(corpus)\n",
    "dic_vocabulary = vectorizer.vocabulary_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sparse Matrix Sample')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(X_train.todense()[:,np.random.randint(0,X_train.shape[1],100)]==0, vmin=0, vmax=1, cbar=False).set_title('Sparse Matrix Sample')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = YChi_train\n",
    "X_names = vectorizer.get_feature_names()\n",
    "p_value_limit = 0.95\n",
    "dtf_features = pd.DataFrame()\n",
    "for cat in np.unique(y):\n",
    "    chi2, p = feature_selection.chi2(X_train, y==cat)\n",
    "    dtf_features = dtf_features.append(pd.DataFrame(\n",
    "                   {'feature':X_names, 'score':1-p, 'y':cat}))\n",
    "    dtf_features = dtf_features.sort_values(['y','score'], \n",
    "                    ascending=[True,False])\n",
    "    dtf_features = dtf_features[dtf_features['score']>p_value_limit]\n",
    "X_names = dtf_features['feature'].unique().tolist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# 1:\n",
      "  . selected features: 114\n",
      "  . top features: republican,trump,president,case,state,coronavirus,biden,election,covid,house,said,thursday,game,mask,democrat,county,health,official,white house,white,public,political,new case,gop,administration,reported,department,cnn,photos,season\n",
      " \n",
      "# 2:\n",
      "  . selected features: 114\n",
      "  . top features: republican,trump,president,case,state,coronavirus,biden,election,covid,house,said,thursday,game,mask,democrat,county,health,official,white house,white,public,political,new case,gop,administration,reported,department,cnn,photos,season\n",
      " \n"
     ]
    }
   ],
   "source": [
    "for cat in np.unique(y):\n",
    "   print(\"# {}:\".format(cat))\n",
    "   print(\"  . selected features:\",\n",
    "         len(dtf_features[dtf_features['y']==cat]))\n",
    "   print(\"  . top features:\", \",\".join(\n",
    "     dtf_features[dtf_features['y']==cat][\"feature\"].values[:30]))\n",
    "   print(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['republican' 'trump' 'president' 'case' 'state' 'coronavirus' 'biden'\n",
      " 'election' 'covid' 'house' 'said' 'thursday' 'game' 'mask' 'democrat'\n",
      " 'county' 'health' 'official' 'white house' 'white' 'public' 'political'\n",
      " 'new case' 'gop' 'administration' 'reported' 'department' 'cnn' 'photos'\n",
      " 'season']\n"
     ]
    }
   ],
   "source": [
    "wq=dtf_features[dtf_features['y']==cat][\"feature\"].values[:30]\n",
    "print(wq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 9958)\t0.20324722008919363\n",
      "  (0, 9759)\t0.19663965529452734\n",
      "  (0, 9755)\t0.17402886504373394\n",
      "  (0, 9683)\t0.17061749336748563\n",
      "  (0, 7865)\t0.2287134970256955\n",
      "  (0, 5897)\t0.28389204190157774\n",
      "  (0, 5361)\t0.28389204190157774\n",
      "  (0, 5360)\t0.21626945053373162\n",
      "  (0, 5309)\t0.17736431504799718\n",
      "  (0, 5134)\t0.29407972150520045\n",
      "  (0, 5133)\t0.24806823860691668\n",
      "  (0, 3771)\t0.29407972150520045\n",
      "  (0, 3756)\t0.16990205528848906\n",
      "  (0, 3045)\t0.28389204190157774\n",
      "  (0, 3043)\t0.23708472819865611\n",
      "  (0, 2416)\t0.251443456139712\n",
      "  (0, 1876)\t0.23062817847478742\n",
      "  (0, 582)\t0.2287134970256955\n",
      "  (1, 9519)\t0.27838031965517707\n",
      "  (1, 8617)\t0.3258028106056098\n",
      "  (1, 7784)\t0.22362246686807077\n",
      "  (1, 6265)\t0.2192190724977832\n",
      "  (1, 6195)\t0.3323643240334428\n",
      "  (1, 6066)\t0.29491401300840314\n",
      "  (1, 5436)\t0.2688398118329369\n",
      "  :\t:\n",
      "  (4456, 329)\t0.22858642166220114\n",
      "  (4456, 328)\t0.22312646785801948\n",
      "  (4457, 9038)\t0.3230127570727369\n",
      "  (4457, 8241)\t0.3696593795344535\n",
      "  (4457, 8239)\t0.26465074810487765\n",
      "  (4457, 7374)\t0.38292488460442864\n",
      "  (4457, 7010)\t0.3029395073491529\n",
      "  (4457, 5295)\t0.2737728264420828\n",
      "  (4457, 4684)\t0.19786137826530928\n",
      "  (4457, 4675)\t0.3696593795344535\n",
      "  (4457, 3737)\t0.3593698544136725\n",
      "  (4457, 549)\t0.26584852154846017\n",
      "  (4458, 9908)\t0.2474609224085818\n",
      "  (4458, 7073)\t0.3564396168369833\n",
      "  (4458, 6761)\t0.2819864842052831\n",
      "  (4458, 6757)\t0.21604770753221506\n",
      "  (4458, 6482)\t0.31434013221099566\n",
      "  (4458, 5334)\t0.27721241991125795\n",
      "  (4458, 4892)\t0.21240362491796888\n",
      "  (4458, 3576)\t0.3564396168369833\n",
      "  (4458, 3575)\t0.22248348919993077\n",
      "  (4458, 3438)\t0.24634599380390493\n",
      "  (4458, 2669)\t0.2819864842052831\n",
      "  (4458, 707)\t0.27501079179723686\n",
      "  (4458, 169)\t0.2690118824801931\n",
      "3281    2\n",
      "5418    2\n",
      "5054    2\n",
      "1389    1\n",
      "2532    1\n",
      "       ..\n",
      "968     2\n",
      "1667    2\n",
      "3321    1\n",
      "1688    1\n",
      "5994    2\n",
      "Name: SUBJpolit, Length: 4459, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(X_train)\n",
    "print(y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    2571\n",
      "2    1888\n",
      "Name: SUBJpolit, dtype: int64\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sentence</th>\n",
       "      <th>SUBJpolit</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>6206</th>\n",
       "      <td>a thursday case covid beaver county including ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6207</th>\n",
       "      <td>texas reported new case third straight day thu...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6208</th>\n",
       "      <td>godinez said called tillis office constituent ...</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>6209</th>\n",
       "      <td>hide caption photos ancient find researcher fo...</td>\n",
       "      <td>1</td>\n",
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       "      <th>6210</th>\n",
       "      <td>public health agency must make recommendation ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6211 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Sentence  SUBJpolit\n",
       "0     yet urgent foreign crisis middle east occupied...          2\n",
       "1     however say even people never develop symptom ...          2\n",
       "2     the government plan roll rating system akin re...          2\n",
       "3     hide caption photos ancient find a licelike in...          1\n",
       "4     the hope probe first interplanetary mission ar...          1\n",
       "...                                                 ...        ...\n",
       "6206  a thursday case covid beaver county including ...          2\n",
       "6207  texas reported new case third straight day thu...          2\n",
       "6208  godinez said called tillis office constituent ...          2\n",
       "6209  hide caption photos ancient find researcher fo...          1\n",
       "6210  public health agency must make recommendation ...          2\n",
       "\n",
       "[6211 rows x 2 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccSet = pd.DataFrame(XChi_train,columns=['Sentence'])\n",
    "ccSet['SUBJpolit'] = YChi_train\n",
    "ccSet = ccSet.reset_index(drop=True)\n",
    "print(ccSet['SUBJpolit'].value_counts())\n",
    "\n",
    "extra = []\n",
    "j=0\n",
    "for sentence in ccSet['Sentence'].values:\n",
    "    if len([i for i in wq if (i in sentence) == True]) != 0:\n",
    "        extra.append([sentence,ccSet['SUBJpolit'][j]])\n",
    "    j=j+1\n",
    "newdf = pd.DataFrame(extra,columns=['Sentence','SUBJpolit'])\n",
    "newdf\n",
    "ccSet = pd.concat([ccSet,newdf]).reset_index(drop=True)\n",
    "ccSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3173\n",
       "2    3038\n",
       "Name: SUBJpolit, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccSet['SUBJpolit'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sentence</th>\n",
       "      <th>SUBJpolit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>and jane confident story its going come back a...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>click expand nick esposito yearold skydiving i...</td>\n",
       "      <td>1</td>\n",
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       "      <td>its still early story even remdesivir approved...</td>\n",
       "      <td>1</td>\n",
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       "      <td>this image released apple tv plus show tom han...</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>kurtulmu also said constantly calling internat...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>8774</th>\n",
       "      <td>want jeopardy contestant take advice game show...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8775</th>\n",
       "      <td>percent novel coronavirus cause covid killed i...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8776</th>\n",
       "      <td>ultimately number target ridley receive season...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8777</th>\n",
       "      <td>we know chinese major general put charge lab s...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8778</th>\n",
       "      <td>speaking cnn last week herald square midtown m...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8779 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Sentence  SUBJpolit\n",
       "0     and jane confident story its going come back a...          1\n",
       "1     click expand nick esposito yearold skydiving i...          1\n",
       "2     its still early story even remdesivir approved...          1\n",
       "3     this image released apple tv plus show tom han...          1\n",
       "4     kurtulmu also said constantly calling internat...          1\n",
       "...                                                 ...        ...\n",
       "8774  want jeopardy contestant take advice game show...          1\n",
       "8775  percent novel coronavirus cause covid killed i...          2\n",
       "8776  ultimately number target ridley receive season...          1\n",
       "8777  we know chinese major general put charge lab s...          1\n",
       "8778  speaking cnn last week herald square midtown m...          2\n",
       "\n",
       "[8779 rows x 2 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# extra = []\n",
    "# j=0\n",
    "# for sentence in b_set['Sentence'].values:\n",
    "#     if len([i for i in wq if (i in sentence) == True]) != 0:\n",
    "#         extra.append([sentence,b_set['SUBJpolit'][j]])\n",
    "#     j=j+1\n",
    "# newdf = pd.DataFrame(extra,columns=['Sentence','SUBJpolit'])\n",
    "# newdf\n",
    "# b_set = pd.concat([b_set,newdf]).reset_index(drop=True)\n",
    "# b_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import transformers as tfs\n",
    "import warnings\n",
    "import joblib\n",
    "warnings.filterwarnings('ignore')\n",
    "cuda = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "# read model from lib\n",
    "model_class, tokenizer_class, pretrained_weights = (tfs.BertModel, tfs.BertTokenizer, 'bert-base-uncased')\n",
    "tokenizer = tokenizer_class.from_pretrained(pretrained_weights)\n",
    "model = model_class.from_pretrained(pretrained_weights)\n",
    "if cuda:\n",
    "    model = model.cuda()\n",
    "    model = nn.DataParallel(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       [101, 1998, 4869, 9657, 2466, 2049, 2183, 2272...\n",
      "1       [101, 11562, 7818, 4172, 9686, 6873, 28032, 20...\n",
      "2       [101, 2049, 2145, 2220, 2466, 2130, 2128, 2687...\n",
      "3       [101, 2023, 3746, 2207, 6207, 2694, 4606, 2265...\n",
      "4       [101, 9679, 5313, 12274, 2036, 2056, 7887, 421...\n",
      "                              ...                        \n",
      "6365    [101, 4092, 13229, 2197, 2733, 9536, 2675, 272...\n",
      "6366    [101, 2122, 20193, 3252, 2081, 14518, 18856, 2...\n",
      "6367    [101, 1996, 5943, 8753, 2121, 12076, 22587, 20...\n",
      "6368    [101, 1996, 3789, 2651, 2498, 9056, 23745, 201...\n",
      "6369    [101, 2021, 2045, 2015, 2130, 5995, 3392, 4064...\n",
      "Name: Sentence, Length: 6370, dtype: object\n",
      "train set shape: (6370, 36)\n",
      "torch.Size([6370, 36, 768])\n"
     ]
    }
   ],
   "source": [
    "# Means to add [CLS] and [SEP] symbols at the beginning and end of the sentence\n",
    "train_tokenized = b_set['Sentence'].apply((lambda x: tokenizer.encode(x, add_special_tokens=True,max_length=77,truncation=True)))\n",
    "print(train_tokenized)\n",
    "#process the sentences into the same length\n",
    "train_max_len = 0\n",
    "for i in train_tokenized.values:\n",
    "    if len(i) > train_max_len:\n",
    "        train_max_len = len(i)\n",
    "train_padded = np.array([i + [0] * (train_max_len-len(i)) for i in train_tokenized.values]) #add 0 sufix\n",
    "print(\"train set shape:\",train_padded.shape)\n",
    "\n",
    "#output：train set shape: (3000, 66)\n",
    "#let the model know which words are not to be processed \"the [PAD] symbol\"\n",
    "train_attention_mask = np.where(train_padded != 0, 1, 0)\n",
    "#in order to improve the training speed, add a series of [PAD] symbols at the end of short sentences:\n",
    "train_input_ids = torch.tensor(train_padded).long()\n",
    "train_attention_mask = torch.tensor(train_attention_mask).long()\n",
    "with torch.no_grad():\n",
    "    train_last_hidden_states = model(train_input_ids, attention_mask=train_attention_mask)\n",
    "print(train_last_hidden_states[0].size())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       [101, 2664, 13661, 3097, 5325, 2690, 2264, 454...\n",
      "1       [101, 2174, 2360, 2130, 2111, 2196, 4503, 2535...\n",
      "2       [101, 1996, 2231, 2933, 4897, 5790, 2291, 1779...\n",
      "3       [101, 5342, 14408, 3258, 7760, 3418, 2424, 103...\n",
      "4       [101, 1996, 3246, 15113, 2034, 6970, 11751, 18...\n",
      "                              ...                        \n",
      "6206    [101, 1037, 9432, 2553, 2522, 17258, 13570, 22...\n",
      "6207    [101, 3146, 2988, 2047, 2553, 2353, 3442, 2154...\n",
      "6208    [101, 2643, 3170, 2480, 2056, 2170, 6229, 2483...\n",
      "6209    [101, 5342, 14408, 3258, 7760, 3418, 2424, 107...\n",
      "6210    [101, 2270, 2740, 4034, 2442, 2191, 12832, 224...\n",
      "Name: Sentence, Length: 6211, dtype: object\n",
      "train set shape: (6211, 36)\n",
      "torch.Size([6211, 36, 768])\n"
     ]
    }
   ],
   "source": [
    "# Means to add [CLS] and [SEP] symbols at the beginning and end of the sentence\n",
    "train_tokenized = ccSet['Sentence'].apply((lambda x: tokenizer.encode(x, add_special_tokens=True,max_length=77,truncation=True)))\n",
    "print(train_tokenized)\n",
    "#process the sentences into the same length\n",
    "train_max_len = 0\n",
    "for i in train_tokenized.values:\n",
    "    if len(i) > train_max_len:\n",
    "        train_max_len = len(i)\n",
    "train_padded = np.array([i + [0] * (train_max_len-len(i)) for i in train_tokenized.values]) #add 0 sufix\n",
    "print(\"train set shape:\",train_padded.shape)\n",
    "\n",
    "#output：train set shape: (3000, 66)\n",
    "#let the model know which words are not to be processed \"the [PAD] symbol\"\n",
    "train_attention_mask = np.where(train_padded != 0, 1, 0)\n",
    "#in order to improve the training speed, add a series of [PAD] symbols at the end of short sentences:\n",
    "train_input_ids = torch.tensor(train_padded).long()\n",
    "train_attention_mask = torch.tensor(train_attention_mask).long()\n",
    "with torch.no_grad():\n",
    "    CStrain_last_hidden_states = model(train_input_ids, attention_mask=train_attention_mask)\n",
    "print(CStrain_last_hidden_states[0].size())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8529111338100103\n",
      "0.8568496664956389\n",
      "0.8608247422680413\n",
      "225\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_classification     \n",
    "from sklearn.linear_model import LogisticRegression  \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.naive_bayes import ComplementNB\n",
    "from sklearn.naive_bayes import BernoulliNB \n",
    "from sklearn.naive_bayes import CategoricalNB\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import roc_curve                \n",
    "from sklearn.metrics import recall_score   \n",
    "from sklearn.metrics import f1_score                 \n",
    "from sklearn.datasets import make_classification    \n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "import joblib\n",
    "training_accuracy = []\n",
    "test_accuracy = []\n",
    "# train_last_hid den_states = joblib.load('../models/6370train_last_hidden_states.b')\n",
    "# svm_clf = SVC(C=0.8, kernel='linear', gamma=20, decision_function_shape='ovr',class_weight=\"balanced\",probability=True)\n",
    "# bnb = BernoulliNB()\n",
    "# gnb = GaussianNB()\n",
    "# lr_clf = joblib.load('../models/lr_clf.clf')\n",
    "train_features = train_last_hidden_states[0][:,0,:].numpy()\n",
    "train_labels = b_set['SUBJpolit']\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(train_features,train_labels.astype('int'),test_size=0.3, random_state=3)\n",
    "X_train = CStrain_last_hidden_states[0][:,0,:].numpy()\n",
    "Y_train = ccSet['SUBJpolit']\n",
    "# gnb_clf = gnb.fit(X_train, Y_train)\n",
    "\n",
    "# print(len(X_test))\n",
    "# print(len(X_test))\n",
    "# lr_clf = LogisticRegression(max_iter=10000,class_weight=\"balanced\")\n",
    "# lr_clf.fit(X_train, Y_train)\n",
    "# svm_clf.fit(X_train, Y_train)\n",
    "# y_pred_prob = lr_clf.predict_proba(X_test)\n",
    "# print(y_pred_prob)\n",
    "# joblib.dump(lr_clf,'../models/6370lr_clf.clf')\n",
    "df_new = pd.DataFrame()\n",
    "df_new['features'] = X_test.tolist()\n",
    "df_new['label'] = Y_test.to_frame().reset_index(drop=True)['SUBJpolit']\n",
    "# lr_clf = joblib.load('../models/6370lr_clf.clf')\n",
    "model = svm_clf\n",
    "y_pred_prob = model.predict_proba(X_test)\n",
    "# y_pred = y_pred_prob[:, 1]\n",
    "# print(y_pred)\n",
    "probs = pd.DataFrame(y_pred_prob.max(axis=1),columns=['prob'])\n",
    "hs = pd.concat((df_new,probs),axis=1)\n",
    "hs1 = hs[hs['prob']>=0.6]\n",
    "hs1_res = model.predict([s for s in hs1['features'].values])\n",
    "print(precision_score(hs1_res,hs1['label'].values.astype('int')))\n",
    "print(f1_score(hs1_res,hs1['label'].values.astype('int')))\n",
    "print(recall_score(hs1_res,hs1['label'].values.astype('int')))\n",
    "print(len(df_new['label']) - len(hs1))\n",
    "# print(y_pred)\n",
    "# y_pred_prob = (y_pred > 0.5).astype('int')\n",
    "# print(y_pred_prob)\n",
    "# print(y_pred_prob)\n",
    "\n",
    "# y_pred_probs = [i+1 if i>1 else 0 for i in y_pred_prob ]\n",
    "\n",
    "# lr_ac = precision_score(Y_test, y_pred_probs, average='weighted')\n",
    "# lr_ac2 = lr_clf.score(X_test,Y_test)\n",
    "# lr_f1 = f1_score(Y_test, y_pred_prob, average='weighted')\n",
    "# lr_rc = recall_score(Y_test, y_pred_prob, average='weighted')\n",
    "# print(lr_ac)\n",
    "# print(lr_ac2)\n",
    "# print(lr_f1)\n",
    "# print(lr_rc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs1 = hs[hs['prob']>0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1911"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2\n",
       "1       2\n",
       "2       1\n",
       "3       1\n",
       "4       1\n",
       "       ..\n",
       "1906    1\n",
       "1907    2\n",
       "1908    2\n",
       "1909    1\n",
       "1910    1\n",
       "Name: label, Length: 1911, dtype: int32"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.78229083, 0.21770917],\n",
       "       [0.34145288, 0.65854712],\n",
       "       [0.95661256, 0.04338744],\n",
       "       ...,\n",
       "       [0.18640074, 0.81359926],\n",
       "       [0.8883107 , 0.1116893 ],\n",
       "       [0.75862053, 0.24137947]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "for i in (y_pred > 0.6).astype('int') :\n",
    "    if i ==1:\n",
    "        print(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1911"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(class_weight='balanced', max_iter=10000)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.naive_bayes import ComplementNB\n",
    "from sklearn.naive_bayes import BernoulliNB \n",
    "from sklearn.naive_bayes import CategoricalNB\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import roc_curve                \n",
    "from sklearn.metrics import recall_score   \n",
    "from sklearn.metrics import f1_score                 \n",
    "from sklearn.datasets import make_classification    \n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "import joblib\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "\n",
    "gnb = GaussianNB()\n",
    "bnb = BernoulliNB()\n",
    "# train_last_hidden_states = joblib.load('../models/4251_clear.bert')\n",
    "# b_set = joblib.load('../models/4251_clear.data')\n",
    "# b_set['Sentence'] = b_set['Sentence'].apply(lambda w:' '.join([WordNetLemmatizer().lemmatize(word.lower()) for word in str(w).split()]))\n",
    "\n",
    "train_features = train_last_hidden_states[0][:,0,:].numpy()\n",
    "train_labels = b_set['SUBJpolit']\n",
    "lr_clf = LogisticRegression(max_iter=10000,class_weight=\"balanced\")\n",
    "svm_clf = SVC(C=0.8, kernel='linear', gamma=20, decision_function_shape='ovr',class_weight=\"balanced\",probability=True)\n",
    "thresset = [0.4,0.5,0.6,0.7,0.8]\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(train_features,train_labels.astype('int'),test_size=0.3, random_state=3)\n",
    "\n",
    "gnb_clf = gnb.fit(X_train, Y_train)\n",
    "bnb_clf = bnb.fit(X_train, Y_train)\n",
    "svm_clf.fit(X_train, Y_train.ravel())\n",
    "lr_clf.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3.922693970765791e-11,\n",
       " 0.999150090326169,\n",
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       " 6.674315839950596e-08,\n",
       " 0.9246061217158368,\n",
       " 1.0,\n",
       " 7.405632369750512e-19,\n",
       " 0.9999999999911893,\n",
       " 1.0488492369840788e-10,\n",
       " 0.9999999999456293,\n",
       " 2.6348407547736714e-05,\n",
       " 2.9826724628712346e-13,\n",
       " 1.3220396241993604e-07,\n",
       " 3.0284123942969298e-37,\n",
       " 1.4193182070508444e-12,\n",
       " 2.331967188038909e-15,\n",
       " 0.9994212456014777,\n",
       " 1.0,\n",
       " 1.0,\n",
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       " 0.9999999999998863,\n",
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       " 1.0,\n",
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       " 1.448938261001106e-23,\n",
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       " 1.0,\n",
       " 1.0,\n",
       " 1.311928525160996e-22,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.341041725012308e-32,\n",
       " 1.7180691153328923e-05,\n",
       " 1.2180111450902875e-24,\n",
       " 2.421540663039505e-12,\n",
       " 1.0,\n",
       " 0.9999999999579501,\n",
       " 3.6198929314846665e-33,\n",
       " 0.9999999999999432,\n",
       " 1.0,\n",
       " 1.744967387938162e-26,\n",
       " 1.0,\n",
       " 0.9938259471507237,\n",
       " 1.0,\n",
       " 0.9999999999994884,\n",
       " ...]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:08<00:00,  1.60s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "aver = 'weighted'\n",
    "training_accuracy = []\n",
    "gnb_test_accuracy = []\n",
    "gnb_test_recall = []\n",
    "gnb_test_fscore = []\n",
    "gnb_drop_item = []\n",
    "bnb_test_accuracy = []\n",
    "bnb_test_recall = []\n",
    "bnb_test_fscore = []\n",
    "bnb_drop_item = []\n",
    "svm_test_accuracy = []\n",
    "svm_test_recall = []\n",
    "svm_test_fscore = []\n",
    "svm_drop_item = []\n",
    "lr_test_accuracy =[]\n",
    "lr_test_recall =[]\n",
    "lr_test_fscore =[]\n",
    "lr_drop_item = []\n",
    "for i in tqdm(thresset):\n",
    "# i=0.8\n",
    "    y_pred = svm_clf.predict_proba(X_test)\n",
    "    y_pred = y_pred[:, 1]\n",
    "    y_pred_prob = (y_pred >  i).astype('int')+1\n",
    "    svm_acu = precision_score(Y_test, y_pred_prob,average=aver)\n",
    "    svm_recall = recall_score(Y_test, y_pred_prob,average=aver)\n",
    "    svm_fscore = f1_score(Y_test, y_pred_prob,average=aver)\n",
    "    svm_test_accuracy.append(svm_acu)\n",
    "    svm_test_recall.append(svm_recall)\n",
    "    svm_test_fscore.append(svm_fscore)\n",
    "    svm_drop_item.append(list((y_pred>i).astype('int')).count(0))\n",
    "    y_pred2 = lr_clf.predict_proba(X_test)\n",
    "    y_pred2 = y_pred2[:, 1]\n",
    "    y_pred_prob2 = (y_pred2 > i).astype('int')+1\n",
    "    lr_accuracy = precision_score(Y_test, y_pred_prob2,average=aver)\n",
    "    lr_recall = recall_score(Y_test, y_pred_prob2,average=aver)\n",
    "    lr_fscore = f1_score(Y_test, y_pred_prob2,average=aver)\n",
    "    lr_test_accuracy.append(lr_accuracy)\n",
    "    lr_test_recall.append(lr_recall)\n",
    "    lr_test_fscore.append(lr_fscore)\n",
    "    lr_drop_item.append(list((y_pred2>i).astype('int')).count(0))\n",
    "\n",
    "    y_pred1 = gnb_clf.predict_proba(X_test)\n",
    "    y_pred1 = y_pred1[:, 1]\n",
    "    y_pred1_prob = (y_pred1 >  i).astype('int')+1\n",
    "    y_pred4 = bnb_clf.predict_proba(X_test)\n",
    "    y_pred4 = y_pred4[:, 1]\n",
    "    y_pred4_prob = (y_pred4 >  i).astype('int')+1\n",
    "    gnb_test_accuracy.append(precision_score(Y_test,y_pred1_prob,average=aver))\n",
    "    gnb_test_recall.append(recall_score(Y_test,y_pred1_prob,average=aver))\n",
    "    gnb_test_fscore.append(f1_score(Y_test,y_pred1_prob,average=aver))\n",
    "    gnb_drop_item.append(list((y_pred1>i).astype('int')).count(0))\n",
    "\n",
    "    bnb_test_recall.append(recall_score(Y_test,y_pred4_prob,average=aver))\n",
    "    bnb_test_fscore.append(f1_score(Y_test,y_pred4_prob,average=aver))\n",
    "    bnb_test_accuracy.append(precision_score(Y_test,y_pred4_prob,average=aver))\n",
    "    bnb_drop_item.append(list((y_pred4>i).astype('int')).count(0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = plt.GridSpec(7, 3, wspace=0.5, hspace=0.5)\n",
    "plt.subplot(grid[0:7,0:3])\n",
    "plt.plot(thresset, lr_test_accuracy, label=\"LR accuracy\")\n",
    "plt.plot(thresset, lr_test_recall, label=\"LR recall\")\n",
    "plt.plot(thresset, lr_test_fscore, label=\"LR f1-score\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.xlabel(\"threshold\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.subplot(grid[0:7,0:3])\n",
    "plt.plot(thresset, svm_test_accuracy, label=\"SVM accuracy\")\n",
    "plt.plot(thresset, svm_test_recall, label=\"SVM recall\")\n",
    "plt.plot(thresset, svm_test_fscore, label=\"SVM f1-score\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.xlabel(\"threshold\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.subplot(grid[0:7,0:3])\n",
    "plt.plot(thresset, gnb_test_accuracy, label=\"GaussianNB accuracy\")\n",
    "plt.plot(thresset, gnb_test_recall, label=\"GaussianNB recall\")\n",
    "plt.plot(thresset, gnb_test_fscore, label=\"GaussianNB f1-score\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.xlabel(\"threshold\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.subplot(grid[0:7,0:3])\n",
    "plt.plot(thresset, bnb_test_accuracy, label=\"BernoulliNB accuracy\")\n",
    "plt.plot(thresset, bnb_test_recall, label=\"BernoulliNB recall\")\n",
    "plt.plot(thresset, bnb_test_fscore, label=\"BernoulliNB f1-score\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.xlabel(\"threshold\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.subplot(grid[0:7,0:3])\n",
    "plt.plot(thresset, lr_drop_item, label=\"LR \")\n",
    "plt.plot(thresset, svm_drop_item, label=\"SVM\")\n",
    "plt.plot(thresset, gnb_drop_item, label=\"GaussianNB\")\n",
    "plt.plot(thresset, bnb_drop_item, label=\"BernoulliNB\")\n",
    "plt.ylabel(\"drop-items\")\n",
    "plt.xlabel(\"threshold\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7597216006585796, 0.761019305132335, 0.7612542467774243, 0.762350368035094, 0.7626749543786003]\n",
      "[0.7519623233908949, 0.7540554683411826, 0.7545787545787546, 0.7561486132914704, 0.7571951857666144]\n",
      "[0.753357566860494, 0.7553949160283409, 0.755896631223772, 0.7574243443134421, 0.7584046878316185]\n",
      "[993, 1001, 1004, 1009, 1017]\n",
      "[0.7877111452959914, 0.7881771020946641, 0.7883324690396826, 0.7860134390199499, 0.7877949515509285]\n",
      "[0.7870225013082156, 0.7880690737833596, 0.7885923600209315, 0.7864992150706437, 0.7885923600209315]\n",
      "[0.7873040772161518, 0.788121125744444, 0.7884484173249405, 0.7862003908397119, 0.7879683333532433]\n",
      "[1086, 1100, 1111, 1119, 1135]\n",
      "[0.8026576664073043, 0.805131755858768, 0.803113528448855, 0.7766097126438277, 0.7557486955452685]\n",
      "[0.7943485086342229, 0.8058608058608059, 0.8006279434850864, 0.7582417582417582, 0.7111459968602826]\n",
      "[0.7955315521640205, 0.8051874546789272, 0.7969498762363689, 0.7448735211926351, 0.6791115573349014]\n",
      "[986, 1140, 1248, 1407, 1559]\n",
      "[0.8077735125399461, 0.804648659349514, 0.80933189552113, 0.8104943473248868, 0.8025739756801481]\n",
      "[0.7980115122972266, 0.8021978021978022, 0.8100470957613815, 0.8095238095238095, 0.7953950811093669]\n",
      "[0.7992068271763847, 0.8028723977103329, 0.8093076855541095, 0.8069751551844394, 0.7894929185785119]\n",
      "[973, 1053, 1144, 1217, 1302]\n"
     ]
    }
   ],
   "source": [
    "print(gnb_test_accuracy) \n",
    "print(gnb_test_recall) \n",
    "print(gnb_test_fscore) \n",
    "print(gnb_drop_item) \n",
    "print(bnb_test_accuracy) \n",
    "print(bnb_test_recall) \n",
    "print(bnb_test_fscore) \n",
    "print(bnb_drop_item) \n",
    "print(svm_test_accuracy) \n",
    "print(svm_test_recall) \n",
    "print(svm_test_fscore) \n",
    "print(svm_drop_item)\n",
    "print(lr_test_accuracy) \n",
    "print(lr_test_recall) \n",
    "print(lr_test_fscore)\n",
    "print(lr_drop_item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1911"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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